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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

MPACT Verification With Magnox Reactor Neutronics Progression Problems

MPACT is a state-of-the-art core simulator designed to perform high-fidelity analysis using whole-core, three-dimensional, pin-resolved neutron transport calculations on modern parallel computing hardware. MPACT was originally developed to model light water reactors, and its capabilities are being extended to simulate gas-cooled, graphite-moderated cores such as Magnox reactors. To verify MPACT’s performance in this new application, the code is being formally benchmarked using representative problems. Progression problems are a series of example models that increase in complexity designed to test a code’s performance. The progression problems include both beginning-of-cycle and depletion calculations. Reference solutions for each progression problem have been generated using Serpent 2, a continuous-energy Monte Carlo reactor physics burnup calculation code.Using the neutron multiplication eigenvalue ke as a metric, MPACT’s performance is assessed on each of the progression problems. Initial results showed that MPACT’s multi-group cross section libraries, originally developed for pressurized water reactor problems, were not sufficient to accurately solve Magnox problems. MPACT’s improved performance on the progression problems is demonstrated using this new optimized cross section library.

Luciano, Nicholas↗
DOE OSTI2024

Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest

The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However, existing approaches only consider precision control on primary data, leaving uncertainties on the quantities of interest (QoIs) derived from it. In this work, we present a progressive data retrieval framework with guaranteed error control on derivable QoIs. Our contributions are three-fold. (1) We carefully derive the theories to strictly control QoI errors during progressive retrieval. Our theory is generic and can be applied to any QoIs that can be composited by the basis of derivable QoIs proved in the paper. (2) We design and develop a generic progressive retrieval framework based on the proposed theories, and optimize it by exploring feasible progressive representations. (3) We evaluate our framework using five real-world datasets with a diverse set of QoIs. Experiments demonstrate that our framework can faithfully respect any user-specified QoI error bounds in the evaluated applications. This leads to over 2.02× performance gain in data transfer tasks compared to transferring the primary data while guaranteeing a QoI error that is less than 1E-5.

Wu, Xuan↗
DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗
DOE OSTI2018

Progress Update on Segmentation of Neutron Tomographic Images, 2018

The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1971

Economic impact of stimulated technological activity. Part 1: Overall economic impact of technological progress: Its measurement

Investigations were performed at the national economic level to explore the aggregate effects of technological progress on economic growth. Inadequacies in existing marco-economic yardsticks forced the study to focus on the cost savings effects achieved through technological progress. The central questions discussed in this report cover: (1) role of technological progress in economic growth, (2) factors determining the rate of economic growth due to technological progress; (3) quantitative measurements of relationships between technological progress, its determinants, and subsequent economic growth; and (4) effects of research and development activities of the space program. For Part 2, see N72-32174.

Source record↗
NASA NTRS2002

Analysis for the Progressive Failure Response of Textile Composite Fuselage Frames

A part of aviation accident mitigation is a crash worthy airframe structure, and an important measure of merit for a crash worthy structure is the amount of kinetic energy that can be absorbed in the crush of the structure. Prediction of the energy absorbed from finite element analyses requires modeling the progressive failure sequence. Progressive failure modes may include material degradation, fracture and crack growth, and buckling and collapse. The design of crash worthy airframe components will benefit from progressive failure analyses that have been validated by tests. The subject of this research is the development of a progressive failure analysis for textile composite. circumferential fuselage frames subjected to a quasi-static, crash-type load. The test data for these frames are reported, and these data, along with stub column test data, are to be used to develop and to validate methods for the progressive failure response.

Johnson, Eric R.↗
NASA NTRS2002

Analysis for the Progressive Failure Response of Textile Composite Fuselage Frames

A part of aviation accident mitigation is a crashworthy airframe structure, and an important measure of merit for a crashworthy structure is the amount of kinetic energy that can be absorbed in the crush of the structure. Prediction of the energy absorbed from finite element analyses requires modeling the progressive failure sequence. Progressive failure modes may include material degradation, fracture and crack growth, and buckling and collapse. The design of crashworthy airframe components will benefit from progressive failure analyses that have been validated by tests. The subject of this research is the development of a progressive failure analysis for a textile composite, circumferential fuselage frame subjected to a quasi-static, crash-type load. The test data for the frame are reported, and these data are used to develop and to validate methods for the progressive failure response.

Johnson, Eric R.↗
NASA NTRS2004

Progressive Response Surfaces

Response surface functions are often used as simple and inexpensive replacements for computationally expensive computer models that simulate the behavior of a complex system over some parameter space. Progressive response surfaces are ones that are built up progressively as global information is added from new sample points in the parameter space. As the response surfaces are globally upgraded based on new information, heuristic indications of the convergence of the response surface approximation to the exact (fitted) function can be inferred. Sampling points can be incrementally added in a structured fashion, or in an unstructured fashion. Whatever the approach, at least in early stages of sampling it is usually desirable to sample the entire parameter space uniformly. At later stages of sampling, depending on the nature of the quantity being resolved, it may be desirable to continue sampling uniformly over the entire parameter space (Progressive response surfaces), or to switch to a focusing/economizing strategy of preferentially sampling certain regions of the parameter space based on information gained in early stages of sampling (Adaptive response surfaces). Here we consider Progressive response surfaces where a balanced indication of global response over the parameter space is desired.We use a variant of Moving Least Squares to fit and interpolate structured and unstructured point sets over the parameter space. On a 2-D test problem we compare response surface accuracy for three incremental sampling methods: Progressive Lattice Sampling; Simple-Random Monte Carlo; and Halton Quasi-Monte-Carlo sequences. We are ultimately after a system for constructing efficiently upgradable response surface approximations with reliable error estimates.

Romero, V. J.↗
NASA NTRS2000

GENOA-PFA: Progressive Fracture in Composites Simulated Computationally

GENOA-PFA is a commercial version of the Composite Durability Structural Analysis (CODSTRAN) computer program that simulates the progression of damage ultimately leading to fracture in polymer-matrix-composite (PMC) material structures under various loading and environmental conditions. GENOA-PFA offers several capabilities not available in other programs developed for this purpose, making it preferable for use in analyzing the durability and damage tolerance of complex PMC structures in which the fiber reinforcements occur in two- and three-dimensional weaves and braids. GENOA-PFA implements a progressive-fracture methodology based on the idea that a structure fails when flaws that may initially be small (even microscopic) grow and/or coalesce to a critical dimension where the structure no longer has an adequate safety margin to avoid catastrophic global fracture. Damage is considered to progress through five stages: (1) initiation, (2) growth, (3) accumulation (coalescence of propagating flaws), (4) stable propagation (up to the critical dimension), and (5) unstable or very rapid propagation (beyond the critical dimension) to catastrophic failure. The computational simulation of progressive failure involves formal procedures for identifying the five different stages of damage and for relating the amount of damage at each stage to the overall behavior of the deteriorating structure. In GENOA-PFA, mathematical modeling of the composite physical behavior involves an integration of simulations at multiple, hierarchical scales ranging from the macroscopic (lamina, laminate, and structure) to the microscopic (fiber, matrix, and fiber/matrix interface), as shown in the figure. The code includes algorithms to simulate the progression of damage from various source defects, including (1) through-the-thickness cracks and (2) voids with edge, pocket, internal, or mixed-mode delaminations.

Murthy, Pappu L. N.↗
NASA NTRS2006

User-Defined Material Model for Progressive Failure Analysis

An overview of different types of composite material system architectures and a brief review of progressive failure material modeling methods used for structural analysis including failure initiation and material degradation are presented. Different failure initiation criteria and material degradation models are described that define progressive failure formulations. These progressive failure formulations are implemented in a user-defined material model (or UMAT) for use with the ABAQUS/Standard1 nonlinear finite element analysis tool. The failure initiation criteria include the maximum stress criteria, maximum strain criteria, the Tsai-Wu failure polynomial, and the Hashin criteria. The material degradation model is based on the ply-discounting approach where the local material constitutive coefficients are degraded. Applications and extensions of the progressive failure analysis material model address two-dimensional plate and shell finite elements and three-dimensional solid finite elements. Implementation details and use of the UMAT subroutine are described in the present paper. Parametric studies for composite structures are discussed to illustrate the features of the progressive failure modeling methods that have been implemented.

Knight, Norman F. Jr.↗
NASA NTRS2023

Incidence and Progression of Chorioretinal Folds During Long-Duration Spaceflight

Importance: The primary contributing factor for development of chorioretinal folds during spaceflight is unknown. Characterizing fold types that develop and tracking their progression may provide insight into the pathophysiology of spaceflight-associated neuro-ocular syndrome and elucidate the risk of fold progression for future exploration-class missions exceeding 12 months in duration. Objective: To determine the incidence and presentation of chorioretinal folds in long-duration International Space Station crew members and objectively quantify the progression of choroidal folds during spaceflight. Design, Setting, and Participants: In this retrospective cohort study, optical coherence tomography scans of the optic nerve head and macula of crew members completing long-duration spaceflight missions were obtained on Earth prior to spaceflight and during flight. A panel of experts examined the scans for the qualitative presence of chorioretinal folds. Peripapillary total retinal thickness was calculated to identify eyes with optic disc edema, and choroidal folds were quantified based on surface roughness within macular and peripapillary regions of interest. Interventions or Exposures: Spaceflight missions ranging 6 to 12 months. Main Outcomes and Measures: Incidence of peripapillary wrinkles, retinal folds, and choroidal folds; peripapillary total retinal thickness; and Bruch membrane surface roughness. Results: A total of 36 crew members were analyzed (mean [SD] age, 46 [6] years; 7 [19%] female). Chorioretinal folds were observed in 12 of 72 eyes (17%; 6 crew members). In eyes with early signs of disc edema, 10 of 42 (24%) had choroidal folds, 4 of 42 (10%) had inner retinal folds, and 2 of 42 (5%) had peripapillary wrinkles. Choroidal folds were observed in all eyes with retinal folds and peripapillary wrinkles. Macular choroidal folds developed in 7 of 12 eyes (4 of 6 crew members) with folds and progressed with mission duration; these folds extended into the fovea in 6 eyes. Circumpapillary choroidal folds developed predominantly superior, nasal, and inferior to the optic nerve head and increased in prevalence and severity with mission duration. Conclusions and Relevance: Choroidal folds were the most common fold type to develop during spaceflight; this differs from reports in idiopathic intracranial hypertension, suggesting differences in the mechanisms underlying fold formation. Quantitative measures demonstrate the development and progression of choroidal folds during weightlessness, and these metrics may help to assess the efficacy of spaceflight-associated neuro-ocular syndrome countermeasures.

Connor R. Ferguson↗
DOE OSTI2019

A progressive deep transfer learning approach to cycle-skipping mitigation in FWI, In SEG Technical Program Expanded Abstracts 2019, pp. 2348-2352. Society of Exploration Geophysicists, 2019.

To effectively mitigate the cycle-skipping phenomenon in full waveform inversion (FWI), we developed an inception-based deep learning neural network to reconstruct the absent low frequency data by exploiting the subsurface low wavenumber information buried in the acquired high frequency data. Two unique features of our deep learning network for the low frequency data reconstruction are 1) Dual Data Feed; 2) Progressive Transfer Learning. With the Dual Data Feed feature, not only the high frequency data, but also the corresponding Beat Tone data are fed into the neural network, significantly reducing the network complexity and the network training cost. The Progressive Transfer Learning technique enables us to initiate the deep learning network with only one training dataset. Unlike other deep learning approaches in this category, our training dataset is not fixed. Instead, the training dataset and the network accuracy are progressively enhanced in an iterative manner within the Progressive Transfer Learning workflow. The numerical experiments validated that the low frequency data reconstructed by this progressive transfer learning network are sufficiently accurate for a successful FWI inversion without suffering from the cycle-skipping without any priori geological information.

58 GEOSCIENCES↗
DOE OSTI2020

Panel Session 89: Progress on Deep Repository Around the World

This panel focused on the progress of deep repository programs, worldwide. While efforts on a deep geological repository for SNF/UNF and HLW in the USA were terminated in 2010, several other repository programs continue to make considerable progress 10 and deserve to be recognized. Panelists from national, multi-national, and international repository programs and organizations reported on the status and future plans as well as the perceived reasons underlying the success and the failure of components of their respective repository program. This session provided a forum for interested parties to present, exchange, and take advantage of, as appropriate, repository progress, challenges, concepts/designs and other lessons learned around the world, including site characterization, site selection, research and development, licensing, construction, operation, and public acceptance and participation. Panelists with presentations: Switzerland's Geological Repository Program for Radioactive Waste (Tim Vietor); Update on NWMO's Progress in Implementing Canada's Plan (Sarah Hirschorn); Cigeo Project Progress and Next Milestones (Corinne Bauer)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

MiR-326 antagomir delays the progression of age-related cataract by upregulating FGF1-mediated expression of betaB2-crystallin

Highlights: • FGF-1 upregulates the expression of an anti-cataract protein βB2. • miR-326 inhibits FGF-1 expression and thereby decreases βB2 level. • miR-326 antagomir delay cataract progression via the function of miR-326. Age-related cataract, the most common cause of blindness worldwide, has been found closely associated with β-crystallin B2 (βB2 or CRYBB2). MicroRNAs (miRNAs) are the primary epigenetic regulators important for various biological processes. However, the role of miRNAs in the progression of lens cataract remains to be elucidated. In this study, we found a novel signal cascade miR-326–fibroblast growth factor 1 (FGF1)–βB2 modulating the progression of lens cataract. In brief, miR-326 exacerbated but its antagomirs attenuated H{sub 2}O{sub 2}-induced apoptosis of HLEC-B3 human lens epithelial cells. Dual-luciferase reporter assay and Western blot showed that miR-326 inhibited FGF1 expression by directly targeting its mRNA 3′-UTR. Consistent with this result, miR-326 antagomir enhanced FGF1 protein level. In addition to FGF1, miR-326 antagomir also enhanced βB2 expression and this enhancement was abolished by transfection of HLEC-B3 cells with FGF1 shRNA. These data demonstrated that miR-326 antagomir increased βB2 expression via upregulating FGF1, which was further confirmed by the studies in a rat model of selenite-induced cataract. This work suggests that miR-326 antagomir might be a promising candidate to prevent progression of age-related cataract.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

The prediction for the internal failure of lithium-ion batteries (LIBs) under external mechanical abuse loading remains a challenge for safe design. This paper systematically studies the modeling approach for progressive failure simulation and short-circuit prediction. Hemispherical indentation experiments are conducted on a battery specimen containing 5 representative sandwich (RS) layers to examine the deformation and progressive failure behavior of battery components. Three different models, viz, a high-fidelity detailed model, an intermediate homogenized model and a fully homogenized model, are developed and employed to simulate the progressive failure in a multi-layer battery specimen. The simulation results obtained using the three models all correlate well with experimental phenomena, capturing the overall stress-strain response and ultimate failure of the battery structure. The numerical results can help us understand the failure behavior and the mechanism for mechanical abuse-induced short circuit in LIBs. The feasibility of the three models for use in progressive failure prediction is compared by analyzing the capability of the models in predicting the critical failure events, the computational efficiency of the models, and the feasibility to implement coupled mechanical-electrochemical-thermal simulations. The results of this work provide useful insights on practical choices for the modeling strategy and safety design of LIBs under mechanical abuse conditions.

25 ENERGY STORAGE↗
DOE OSTI2021

Effect of disease progression on the podocyte cell cycle in Alport Syndrome

Progression of glomerulosclerosis is associated with loss of podocytes with subsequent glomerular tuft instability. It is thought that a diminished number of podocytes may be able to preserve tuft stability through cell hypertrophy associated with cell cycle reentry. At the same time, reentry into the cell cycle risks podocyte detachment if podocytes cross the G1/S checkpoint and undergo abortive cytokinesis. In order to study cell cycle dynamics during chronic kidney disease (CKD) development, we used a FUCCI model (fluorescence ubiquitination-based cell cycle indicator) of mice with X-linked Alport Syndrome. This model exhibits progressive CKD and expresses fluorescent reporters of cell cycle stage exclusively in podocytes. With the development of CKD, an increasing fraction of podocytes in vivo were found to be in G1 or later cell cycle stages. Podocytes in G1 and G2 were hypertrophic. Heterozygous female mice, with milder manifestations of CKD, showed G1 fraction numbers intermediate between wild-type and male Alport mice. Proteomic analysis of podocytes in different cell cycle phases showed differences in cytoskeleton reorganization and metabolic processes between G0 and G1 in disease. Additionally, in vitro experiments confirmed that damaged podocytes reentered the cell cycle comparable to podocytes in vivo. Importantly, we confirmed the upregulation of PDlim2, a highly expressed protein in podocytes in G1, in a patient with Alport Syndrome, confirming our proteomics data in the human setting. Thus, our data showed that in the Alport model of progressive CKD, podocyte cell cycle distribution is altered, suggesting that cell cycle manipulation approaches may have a role in the treatment of various progressive glomerular diseases characterized by podocytopenia.

59 BASIC BIOLOGICAL SCIENCES↗